Publications

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El Emary, I., and A. - E. Hassanien, "Guest editorial special issue on intelligent multimedia computing", Telecommunication Systems, vol. 40, no. 1: Springer, pp. 1–4, 2009. Abstract
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El Emary, I., and A. - E. Hassanien, "Guest editorial special issue on intelligent multimedia computing", Telecommunication Systems, vol. 40, no. 1: Springer, pp. 1–4, 2009. Abstract
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Ibrahim, R. A., H. A. Hefny, and A. E. Hassanien, "Group Impact: Local Influence Maximization in Social Networks", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 447–455, 2016. Abstract
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Fouad, M. M., A. I. Hafez, A. E. Hassanien, and V. Snasel, "Grey Wolves Optimizer-based Localization Approach in WSNs", IEEE iInternational Computer Engineering Conference - ICENCO , Bilbao, Spain, 30 Dec, 2015.
Fouad, M. M., A. I. Hafez, A. E. Hassanien, and V. Snasel, "Grey Wolves Optimizer-based localization approach in WSNs", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 256–260, 2015. Abstract
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Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Grey wolf optimizer-based back-propagation neural network algorithm", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

For many decades, artificial neural network (ANN) proves successful results in thousands of problems in many disciplines. Back-propagation (BP) is one of the candidate algorithms to train ANN. Due to the way of BP to find the solution for the underlying problem, there is an important drawback of it, namely the stuck in local minima rather than the global one. Recent studies introduce meta-heuristic techniques to train ANN. The current work proposes a framework in which grey wolf optimizer (GWO) provides the initial solution to a BP ANN. Five datasets are used to benchmark GWO BP performance with other competitors. The first competitor is an optimized BP ANN based on genetic algorithm. The second is a BP ANN powered by particle swarm optimizer. The third is the BP algorithm itself and lastly a feedforward ANN enhanced by GWO. The carried experiments show that GWOBP outperforms the compared algorithms.

Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Grey wolf optimizer-based back-propagation neural network algorithm", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 213–218, 2016. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, A. E. -karim Mohamed, and O. Hegazy, "Grey wolf optimizer and case-based reasoning model for water quality assessment", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt, Nov. 28-30, 2015. Abstract

This paper presents a bio-inspired optimized classification model for
assessing water quality. As fish gills histopathology is a good biomarker for indicating
water pollution, the proposed classification model uses fish gills microscopic
images in order to asses water pollution and determine water quality.
The proposed model comprises five phases; namely, case representation for
defining case attributes via pre-processing and feature extraction steps, retrieve,
reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms
have been utilized for feature extraction stage. Case-based reasoning
(CBR) has been employed, along with the bio-inspired Gray Wolf Optimization
(GWO) algorithm, for optimizing feature selection and the k case retrieval parameters
in order to asses water pollution. The datasets used for conducted experiments
in this research contain real sample microscopic images for fish gills
exposed to copper and water pH in different histopathlogical stages. Experimental
results showed that the average accuracy achieved by the proposed GWO-CBR
classification model exceeded 97.2% considering variety of water pollutants.

Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. - K. Mohamed, "Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 229–239, 2016. Abstract
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Mostafa, A., A. E. Hassanien, and H. A. Hefny, "Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 562–581, 2017. Abstract
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Esraa Elhariri, N. El-Bendary, and A. A. Aboul Ella Hassanien, "Grey Wolf Optimization for One-Against-One Multi-class Support Vector Machines", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015. Abstract

Grey Wolf Optimization (GWO) algorithm is a
new meta-heuristic method, which is inspired by grey wolves,
to mimic the hierarchy of leadership and grey wolves hunting
mechanism in nature. This paper presents a hybrid model that
employs grey wolf optimizer (GWO) along with support vector
machines (SVMs) classification algorithm to improve the classification
accuracy via selecting the optimal settings of SVMs
parameters. The proposed approach consists of three phases;
namely pre-processing, feature extraction, and GWO-SVMs
classification phases. The proposed classification approach was
implemented by applying resizing, remove background, and
extracting color components for each image. Then, feature
vector generation has been implemented via applying PCA
feature extraction. Finally, GWO-SVMs model is developed
for selecting the optimal SVMs parameters. The proposed
approach has been implemented via applying One-againstOne
multi-class SVMs system using 3-fold cross-validation. The
datasets used for experiments were constructed based on real
sample images of bell pepper at different stages, which were
collected from farms in Minya city, Upper Egypt. Datasets
of total 175 images were used for both training and testing
datasets. Experimental results indicated that the proposed
GWO-SVMs approach achieved better classification accuracy
compared to the typical SVMs classification algorithm.

Esraa Elhariri, N. El-Bendary, A. E. Hassanien, and A. Abraham, "Grey wolf optimization for one-against-one multi-class support vector machines", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 7–12, 2015. Abstract
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Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Graph Partitioning based Automatic Segmentation Approach for CT Scan Liver Images", IEEE Federated Conference on Computer Science and Information Systems, pp. 205–208, Wroclaw - Poland , 9-13 Sept, 2012. Abstractgraph_partitioning_based_automatic_segmentation.pdf

Manual segmentation of liver computerized tomography (CT) images is very time consuming, so it is desired to develop a computer-based approach for the analysis of liver
CT images that can precisely segment the liver without any human intervention. This paper presents normalized cuts graph partitioning approach for liver segmentation from CT images. To evaluate the performance of the presented approach, we present tests on different liver CT images. Experimental results obtained show that the overall accuracy offered by the employed normalized cuts technique is high compared to the well known K-means segmentation approach.

Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Graph partitioning based automatic segmentation approach for ct scan liver images", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 183–186, 2012. Abstract
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Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Graph partitioning based automatic segmentation approach for ct scan liver images", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 183–186, 2012. Abstract
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Karam, H., A. - E. Hassanien, and M. Nakajima, "Graph grammar algebraic approach for generating fractal patterns", WSCG99 Inter. Conf., Czech Republic, pp. 492–500, 1999. Abstract
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Basha, S. H., A. S. Abdalla, and A. E. Hassanien, "GNRCS: Hybrid Classification System based on Neutrosophic Logic and Genetic Algorithm", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 53–58, 2016. Abstract
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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Genetic annealing optimization: Design and real world applications", Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 1: IEEE, pp. 183–188, 2008. Abstract
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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Genetic annealing optimization: Design and real world applications", Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 1: IEEE, pp. 183–188, 2008. Abstract
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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy:, "Genetic Algorithms for Multi-Objective Community Detection in Complex Networks.", Social Networks: A Framework of Computational Intelligence , London, Volume 526, pp 145-171, Springer, 2014.
Ahmed Ibrahim Hafez, N. Ghali, A. E. Hassanien, and A. Fahmy, "Genetic Algorithms for Multi-Objective Community Detection in Complex Networks ", IEEE International Conference on Intelligent Systems Design and Applications (ISDA) , Kochi, India, pp. 460 - 465, Nov. 27-29 2012. Abstract

Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce.

Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for multi-objective community detection in complex networks", Social Networks: A Framework of Computational Intelligence: Springer International Publishing, pp. 145–171, 2014. Abstract
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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for multi-objective community detection in complex networks", Social Networks: A Framework of Computational Intelligence: Springer International Publishing, pp. 145–171, 2014. Abstract
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Hafez, A. I., N. I. Ghali, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for community detection in social networks", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 460–465, 2012. Abstract
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Hafez, A. I., N. I. Ghali, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for community detection in social networks", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 460–465, 2012. Abstract
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